QoS-aware web service selection with negative selection algorithm

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.     

Genetic programming for QoS-aware web service composition and selection

Web services, which can be described as functionality modules invoked over a network as part of a larger application are often used in software development. Instead of occasionally incorporating some of these services in an application, they can be thought of as fundamental building blocks that are combined in a process known as Web service composition. Manually creating compositions from a large number of candidate services is very time consuming, and developing techniques for achieving this objective in an automated manner becomes an active research field. One promising group of techniques encompasses evolutionary computing, which can effectively tackle the large search spaces characteristic of the composition problem. Therefore, this paper proposes the use of genetic programming for Web service composition, investigating three variations to ensure the creation of functionally correct solutions that are also optimised according to their quality of service. A variety of comparisons are carried out between these variations and two particle swarm optimisation approaches, with results showing that there is likely a trade-off between execution time and the quality of solutions when employing genetic programming and particle swarm optimisation. Even though genetic programming has a higher execution time for most datasets, the results indicate that it scales better than particle swarm optimisation.     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.     

Consumer-centric QoS-aware selection of web services

There exist many web services which exhibit similar functional characteristics. It is imperative to provide service consumers with facilities for selecting required web services according to their non-functional characteristics or quality of service (QoS). However, the selection process is greatly complicated by the distinct views of service providers and consumers on the services QoS. For instance, they may have distinct views of the service reliability—wherein a consumer considers that a service is reliable if its success rate is higher than 99%, while a provider may consider its service as reliable if its success rate is higher than 90%. The aim of this paper is to resolve such conflicts and to ensure consensus on the QoS characteristics in the selection of web services. It proposes a QoS Consensus Moderation Approach (QCMA) in order to perform QoS consensus and to alleviate the differences on QoS characteristics in the selection of web services. The proposed approach is implemented as a prototype tool and is tested on a case study of a hotel booking web service. Experimental results show that the proposed approach greatly improves the service selection process in a dynamic and uncertain environment of web services.     

结合语义与事务属性的QoS感知的服务优化选择

服务级别协议（SLA）等级感知的服务选择是NP难题。针对服务选择中维度与粒度方面的问题,提出结合语义与事务属性的服务质量（QoS）感知的服务优化选择模型。该模型从语义链接匹配度、QoS与事务三个维度对服务进行优化选择,并设计了支持多粒度的编码策略。针对服务选择中时间复杂度高的问题,提出了克隆选择与遗传算法相结合的混合优化算法。该算法首先采用动态适应度函数,逐代淘汰不满足约束的个体;其次给出了事务属性的优先级,并根据优先级设计了知识启发式的交叉与变异算子,以保证个体满足事务属性要求;最后在遗传算法中对优秀个体进行克隆选择,以增强对最优解的搜索能力。仿真实验中,该算法在服务选择的精确度和成功率方面均优于遗传算法;在时间花费上稍高于遗传算法但远低于穷举法。实验结果表明,所提算法能在较少时间花费的基础上保证服务选择的质量。     

Engineering self-organizing referral networks for trustworthy service selection

Developing, maintaining, and disseminating trust in open, dynamic environments is crucial. We propose self-organizing referral networks as a means for establishing trust in such environments. A referral network consists of autonomous agents that model others in terms of their trustworthiness and disseminate information on others' trustworthiness. An agent may request a service from another; a requested agent may provide the requested service or give a referral to someone else. Possibly with its user's help, each agent can judge the quality of service obtained. Importantly, the agents autonomously and adaptively decide with whom to interact and choose what referrals to issue, if any. The choices of the agents lead to the evolution of the referral network, whereby the agents move closer to those that they trust. This paper studies the guidelines for engineering self-organizing referral networks. To do so, it investigates properties of referral networks via simulation. By controlling the actions of the agents appropriately, different referral networks can be generated. This paper first shows how the exchange of referrals affects service selection. It identifies interesting network topologies and shows under which conditions these topologies emerge. Based on the link structure of the network, some agents can be identified as authorities. Finally, the paper shows how and when such authorities emerge. The observations of these simulations are then formulated into design recommendations that can be used to develop robust, self-organizing referral networks.     

On optimal decision for QoS-aware composite service selection

The increasing popularity of employing web services for distributed systems contributes to the significance of service discovery. However, duplicated and similar functional features existing among services require service consumers to include additional aspects to evaluate the services. Generally, the service consumers would have different view on the quality of service (QoS) of service attributes. How to select the best composite service in theory among available service (WS) candidates for consumers is an interesting practical issue. This work proposes a QoS-aware service selection model based on fuzzy linear programming (FLP) technologies, in order to identify their dissimilarity on service alternatives, assist service consumers in selecting most suitable services with consideration of their expectations and preferences. This approach can obtain the optimal solution of consensual weight of QoS attribute and fuzzy positive ideal solution (FPIS) by extending LINMAP method, developed by Srinivasan and Shocker. Finally, two numerical examples are given to demonstrate the process of QoS-aware web service selection. The experimental results demonstrated that it is a feasible and supplementary manner in selecting the of web services.     

Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition

Web service composition combines available services to provide new functionality. The various available services have different quality-of-service (QoS) attributes. Building a QoS-optimal web service composition is a multi-criteria NP-hard problem. Most of the existing approaches reduce this problem to a single-criterion problem by aggregating different criteria into a unique global score (scalarization). However, scalarization has some significant drawbacks: the end user is supposed to have a complete a priori knowledge of its preferences/constraints about the desired solutions and there is no guarantee that the aggregated results match it. Moreover, non-convex parts of the Pareto set cannot be reached by optimizing a convex weighted sum. An alternative is to use Pareto-based approaches that enable a more accurate selection of the end-user solution. However, so far, only few solutions based on these approaches have been proposed and there exists no comparative study published to date. This motivated us to perform an analysis of several state-of-the-art multi-objective evolutionary algorithms. Multiple scenarios with different complexities are considered. Performance metrics are used to compare several evolutionary algorithms. Results indicate that GDE3 algorithm yields the best performances on this problem, also with the lowest time complexity.     

QoS-aware service selection via collaborative QoS evaluation

We present in this paper a novel collaborative filtering based scheme for evaluating the QoS of large scale Web services. The proposed scheme automates the process of assessing the QoS of a priori unknown service providers and thus facilitates service users in selecting services that best match their QoS requirements. Most existing service selection approaches ignore the great diversity in the service environment and assume that different users receive identical QoS from the same service provider. This may lead to inappropriate selection decisions as the assumed QoS may deviate significantly from the one actually received by the users. The collaborative filtering based approach addresses this issue by taking the diversity into account instead of uniformly applying the same QoS value to different users. They predict a user’s QoS on an unknown service by exploiting the historical QoS experience of similar users. Nevertheless, when only limited historical QoS data is available, these approaches either fail to make any predictions or make very poor ones. The cornerstone of the proposed QoS evaluation scheme is a Relational Clustering based Model (or RCM) that effectively addresses the data scarcity issue as stated above. Experimental results on both real and synthetic datasets demonstrate that the proposed scheme can more accurately predict the QoS on unknown service providers. The efficient performance also makes it applicable to QoS evaluation for large scale Web services.     

A structural-semantic web service selection approach to improve retrievability of web services

Service-Oriented Computing promotes building applications by consuming and reusing Web Services. However, the selection of adequate Web Services given a client application is still a major challenge. The effort of assessing and adapting candidate services could be overwhelming due to the “impedance” of Web Service interfaces expected by clients versus the actual interfaces of retrieved Web Services. In this work, we present a novel structural-semantic approach to help developers in the retrieval and selection of services from a service registry. The approach is based on a comprehensive structural scheme for service Interface Compatibility analysis, and WordNet as the semantic support to assess identifiers of operations and parameters. We also empirically analyze, compare and contrast the performance of three service selection methods: a pure structural approach, a pure semantic approach, and the structural-semantic (hybrid) approach proposed in this work. The experimental analysis was performed with two data-sets of real-world Web Services and a service discovery support already published in the literature. Results show that our hybrid service selection approach improved effectiveness in terms of retrievability of Web Services compared to the other approaches.     

基于QoS的组合服务优化选择问题建模与求解

提出了一种支持Web服务动态组合的框架,并在此基础上对Web服务组合中的服务优化选择问题建模,根据用户设置的QoS约束条件,将服务优化选择问题分成3类,并分别给出了相应的服务选择算法。实验证明算法在保证一定优度的同时亦具有良好的时间性能。     

GOS: a global optimal selection strategies for QoS-aware web services composition

Services composition technology provides a promising way to create new services in services-oriented architecture. However, some challenges are hindering the application of services composition. One of the major challenges encountered by composite services developer is how to effectively select a set of services across different autonomous regions (e.g., organization or business) to instantiate a composite service which can satisfy user’s QoS constraints. To solve QoS-aware Web service composition problem, this paper proposes a global optimization selection (GOS) approach based on prediction mechanism for QoS values of local services. The GOS includes two parts. First, the local preprocessing service selection algorithm can be used to increase composite services performance in run-time by predicting the change of service quality parameters. Second, GOS aims at enhancing the run-time performance of global selection by reducing QoS aggregation operations. The simulation results show that the GOS has excellent selection and lower execution cost than existing approaches.     

User-centered design of a QoS-based web service selection system

In most of quality of service (QoS)-based web service selection systems, inexperienced end users are not the focal point of the design. Most systems assume that users could formulate their QoS requirements easily and are accurately using the provided query languages. However, oftentimes it is not true. To tackle this issue and emphasize more on the user-centered design of the service selection system, in this paper, we come up with a more expressive and flexible way for non-expert users to define their QoS queries, together with the user support on formulating queries and understanding services in the registry. Then, we propose an enhanced selection model that could handle both exact and fuzzy requirements, return two categories of matching offers—super-exact and partial matches, and rank them using a two-level ranking algorithm. A prototype system is implemented, and experiments are done using a real QoS dataset to show the system performance.     

An evidence-based scheme for web service selection

Existing studies on the web service selection problem focus mainly on the functional QoS properties of the service rather than the consumer satisfaction and trust aspects. While a good QoS enhances the reputation of a service, different consumers invariably hold differing views of the service contents. Some service reputation approaches primarily consider the consumer’s prior experience of the service via opinion feedback system, may neglect the effect of social trust transition in the recommendations of others. As a result, the problem of reaching consensus on the level of consumer trust regarding service becomes one of key issues in service selection. This study proposes a trust-based service selection model to estimate the degree of consumer trust in a particular service based on the consumers’ direct experience and indirect recommendation of the service. In the proposed approach, the degree of consumer trust is correctly estimated by extending Dempster–Shafer evidence reasoning theory to the reputation computation using consumers’ direct experience and incorporating Jøsang’s belief model for solving the trust transition problem in the indirect recommendation of the service. The proposed model effectively enables deception detection by means of existing bodies of evidence, and therefore excludes the fraudulent evidence of malicious evaluators from the selection process. In addition, a quality index is proposed to help third party (TTP) examine the body of evidence and make the outranking result more reliable. Importantly, the quality index is based not only on the confidence degree of the evidence, but also on the support degree, and therefore discovers the effects of intentional negative assessments. The validity of the proposed approach is demonstrated numerically by means of two service selection examples.     

A context-aware approach for trustworthy worker selection in social crowd

Crowdsourcing applications like Amazon Mechanical Turk (AMT) make it possible to address many difficult tasks (e.g., image tagging and sentiment analysis) on the internet and make full use of the wisdom of crowd, where worker quality is one of the most crucial issues for the task owners. Thus, a challenging problem is how to effectively and efficiently select the high quality workers, so that the tasks online can be accomplished successfully under a certain budget. The existing methods on the crowd worker selection problem mainly based on the quality measurement of the crowd workers, those who have to register on the crowdsourcing platforms. With the connect of the OSNs and the crowdsourcing applications, the social contexts like social relationships and social trust between participants and social positions of participants can assist requestors to select one or a group of trustworthy crowdsourcing workers. In this paper, we first present a contextual social network structure and a concept of Strong Social Component (SSC), which emblems a group of workers who have high social contexts values. Then, we propose a novel index for SSC, and a new efficient and effective algorithm C-AWSA to find trustworthy workers, who can complete the tasks with high quality. The results of our experiments conducted on four real OSN datasets illustrate that the superiority of our method in trustworthy worker selection.     

快速语义Web服务发现方法

针对现有的基于本体描述的语义Web服务发现方法发现效率较为低下的问题,提出一种新的服务发现方法.该方法在基于本体距离计算语义Web服务综合相似度的基础上,利用数据挖掘中的聚类算法AGNES对语义Web服务集合进行聚类预处理,形成若干服务簇,然后应用相应服务发现算法根据相似度阈值定位于某一服务簇内进行查找匹配,从而可减少搜索空间.理论与仿真实验结果表明,该方法既可保证服务发现的准确率,又可明显提高服务发现的效率.     

Social context-aware trust paths finding for trustworthy service provider selection in social media

Multimedia Tools and Applications - Online Social Network (OSN) has been used to enhance service provision and service selection, where trust is one of the most important factors for the decision...